The following is a first proposal for a basic layout. This is not yet complete, ideas are welcome. Discuss on the talk page or just add them here.

The book is laid out into 5 sections, with increasing detail and complexity. Each section contains a number of chapters. In addition to regular chapters, there are case-study chapters that investigate full and complex AI systems using several techniques from the regular chapters (as well as perhaps some new ones).

Planning, Decision making and Problem Solving: Expanding on the search chapter to show how simple agents and simple intelligent behavior can be created. Examples are solving a puzzle, navigating a small maze (with pits and monsters) or planning a trip to the supermarket.

Uncertainty: Introduction to reasoning, planning and decision making with uncertainty.

Case Study - Building a (relatively) strong game AI: Building a strong AI for some game (to be chosen) that combines techniques from the planning and uncertainty chapters. This should go deeper than the simplified algorithms that most books describe and actually produce a strong playing AI.

Case Study - Artificial Life: Describes an environment with several evolving agents and some different techniques to construct agents. This should be able to draw on and compare pretty much all the chapters from section 2 (including the natural language chapter).

Philosophical Issues for Artificial Intelligence: Discusses AI problems typically dealt with by philosophers, such as what is theoretically possible to achieve with AI, whether programs can be conscious, or feel pain, intentionality, and symbol grounding.

Advanced Expert Systems: Expands on the basic expert systems explained in Knowledge Engineering in section 2. Includes more in depth explanation of Bayesian Networks than in the Machine Learning section.

Case Study - Data Mining: Describes mining a large dataset (perhaps some part of the wikipedia database) using machine learning algorithms, using software like Weka.

Advanced Natural Language: A description of the various techniques for dealing with tenses, sentence focus, presuppositions, etc. in NLP and NLG. This focuses mostly on the underlying structure of language and how to translate into some logical language, rather than statistical methods and models.

Case Study - Dialogue System: Building system that can communicate (intelligently) in written natural language. In a nutshell, trying to pass the Turing test. Three basic paradigms; case based reasoning (like ALICE), Logic based (translating everything to and from some extended version of predicate logic) and some machine learning based solution.